Data Services

Data services also called as Data as a service referred as a small independent and also very loosely coupled functions which will enhance, organise and share or may calculate information collected and saved in data storage volumes.Data services will amplify traditional data by improving its resilience, availability and also validity which is used for adding the characteristics to data which will not already have a native like metadata. Data service architectures can involve multiple kinds of data and application services working together to achieve a goal like intelligent data as a service architecture.

How the Data does services work?

Data services are called as self contained units of software functions which give data characteristics it doesn’t already will have. Data services may make data more available, resilient and comprehensible that make data more useful to the users and programs. Data service functions turn inputs into outputs. These inputs are varied sets of any raw data where the data hasn’t been processed for a specific purpose configured in its native format and also saved in physical, virtual or may be cloud based storage volumes.

The outputs are :

Organizational- There is consolidation, management, batching and structure of data usually pulled from structured, unstructured sources.

Transferable- There is movement of data from their place of origin across a network to an endpoint like an application or platform.

Procedural- The processing of data, usually as a part of data modelling, analytics or may be artificial intelligence/machine learning software.

For what these data services are used?

Managing the stored data

Here the data services can help to manage data at rest in other words data saved in storage volumes. Data services abstract raw data from their sources like customer records from online transactional processing databases, property damage information from databases property damage information from data warehouses and images or videos from the data lake and apply governance principles, organisation and maintenance that makes data useful to applications and accessible by users. Data services are an important part of big data strategies because they make some sense of massive collections of structured and unstructured data stored all over the place.

Moving the data:

Data services will be used for data in motion, as it moves from the storage origin to the application or platform usually in real time. Data services can create data pipelines to help data move continuously between many endpoints. For example, data services will help the organization shift from the batch oriented data processing to event-driven data processing by the operating any data which is generated.

Using the data:

Data services which will help to activate data to use in data science, data analytics and also data modelling software. Data services will help improve data access to high performance, intelligent data processing platforms like AI/ML and also deep learning tools that will be depending on the data service, data in the action cloud involve collections of small independent and loosely coupled services usually packaged in the containers.

Difference between traditional storage and data storage:

Traditional storage:

There are actual collections and also retention of any raw digital transformation the bits and also bytes behind the applications, network protocols, documents, media, address books, user preferences. When we save a document and select a location we are going through the process of data storage. A user’s view into data storage is commonly at the infrastructure level and seldom connected between storage volumes. There is usually not any native way to view every file, block or object saved across the workstation, cloud storage provider and external hard drive knowing act by knowing the data storage very manual and monolithic.

Data storage:

Software uses the data stored in traditional data storage volumes as inputs. This creates specific outputs or software that amplifies traditional data by improving its resiliency, availability and validity. Users typically interact with the data services as a part of application making the process very flexible and customisable. For example the data service provided by Red hat or open shift data foundation will abstracts storage infrastructure so data can be stored in many different places but act as a single persistent repository.